Interested in this AI/ML Engineer role at Apptegy?
Apply Now →Skills & Technologies
About This Role
Who We Are
At Apptegy, we are more than a tech company; we are partners dedicated to transforming how schools communicate and shape the future of education. Your work here will directly empower districts to share their stories, engage their communities, and celebrate student success. We're a team of thoughtful, high-performing individuals committed to making a tangible impact. If you're looking for a dynamic environment where you'll be supported with exceptional mentorship and resources to grow your career, come build with us.The Role
We are hiring an AI Automation Engineer who thrives on building durable, fast-moving solutions that unlock operational efficiency and connect people, processes, and tools across the functions that run the company.
In this role, you will serve as a hands-on builder and cross-functional partner, working with leaders and process owners across finance and operations to design and implement AI-powered workflows that make our teams more effective. You will focus on using tools like Claude and Claude Code, N8N, Glean, and direct API work against NetSuite, Salesforce, and Snowflake to automate, integrate, and scale how we work without needing to reinvent the wheel.
This is not a traditional engineering role. It is ideal for someone who loves solving complex problems at the intersection of technology and business operations, a true solutions engineer who is just as comfortable connecting APIs as sitting with an accountant to learn why a process works the way it does. You will also work closely with the AI Champions and builders already embedded across the business to align efforts, ensure adoption, and share best practices company-wide.
What You'll Do
- Find the work by partnering with process owners to map how work actually happens today, including the exceptions, the workarounds, and the steps that only live with a user. Much of the highest-value work will be work nobody filed a request for.
- Size it before you build it by defining the metric and stating the expected result up front, whether that is hours removed, cycle time, error rate, or cost avoided, then measuring the actual result once it ships.
- Build and automate smart workflows using AI agents and skills, API integrations, SQL and Python, workflow orchestration, and scripting against the systems where the work lives, taking each one from prototype to production.
- Own the connective layer by moving data cleanly between NetSuite, Salesforce, DealHub, Workday, Greenhouse, Snowflake, and the rest of the stack through APIs, webhooks, and pipelines, including the reconciliations that prove both sides agree.
- Multiply the builders already in each function by publishing the shared primitives, patterns, and templates they need, so capability compounds instead of queueing behind one person, and by maintaining the automation inventory, including retiring what no longer earns its place.
What You'll Bring
- 2-4+ years of experience in a technical, operations, or automation-focused role such as solutions engineering, business systems, RevOps, or technical program management..
- Working proficiency in at least one scripting language, plus experience integrating systems via APIs, webhooks, and data flows.
- Hands-on production experience with modern AI tooling such as Claude / Claude Code, Cursor, agent frameworks, MCP, or N8N, and informed opinions about when each is the wrong choice.
- Real depth in at least one enterprise business system and its data model.
- Strong cross-functional collaboration skills with the ability to translate business needs into clear, actionable technical solutions.
- A builder's mindset that is creative, curious, and eager to experiment with new technologies to drive efficiency.
- Excellent communication skills and comfort working with multiple stakeholders in a fast-moving environment.
What Makes You Stand Out
- Experience in B2B SaaS, EdTech, or fintech industries.
- Familiarity with scripting or data tools such as Python, JavaScript, or SQL for customization and troubleshooting.
- Experience across a finance stack or a GTM stack (ERP, CRM, CPQ, support platforms).
- Understanding of AI-powered automation systems and data enrichment tools.
Why Apptegy
Join a team that's committed to your success. At Apptegy, we're passionate about creating an environment where you can do your best work and find true fulfillment. We believe in investing in our people—both professionally and personally—because your well-being drives our collective impact.
US Employee Benefits: Comprehensive medical, dental, vision, and life insurance coverage Retirement 401(k) with employer match Health Savings Accounts (HSA) and Flexible Spending Accounts (FSAs) Mental Health Reimbursement Unlimited paid time off, including seasonal (December) company-wide time off Paid parental and medical leave
MX Employee Benefits: Private medical insurance for you and your dependents
Life insurance
15 days Aguinaldo
Vales de Despensa
Fondo de Ahorro
Caja de Ahorro
Flexible paid time off policy
Paid travel to/from Little Rock, Arkansas for Onboarding.
Apptegy champions the thoughtful integration of AI to empower our teams and processes. As we seek to understand your individual capabilities and how you might contribute, we ask that all responses to application questions and during interviews are genuinely your own. Please refrain from using AI generation tools, as our aim is to assess your authentic voice and expertise.
Equal Opportunity Employer Apptegy is an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, gender expression, national origin, age, protected veteran or disabled status, or genetic information.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Apptegy, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Apptegy AI Hiring
Apptegy has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Little Rock, AR, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.